Overview

Dataset statistics

Number of variables18
Number of observations31925
Missing cells26101
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 MiB
Average record size in memory152.0 B

Variable types

Numeric13
Categorical5

Alerts

aids_diagnoses is highly overall correlated with aids_diagnosis_rate and 5 other fieldsHigh correlation
aids_diagnosis_rate is highly overall correlated with aids_diagnoses and 6 other fieldsHigh correlation
borough is highly overall correlated with uhfHigh correlation
concurrent_diagnoses is highly overall correlated with aids_diagnoses and 4 other fieldsHigh correlation
death_rate is highly overall correlated with aids_diagnoses and 3 other fieldsHigh correlation
deaths is highly overall correlated with aids_diagnoses and 7 other fieldsHigh correlation
gender is highly overall correlated with aids_diagnosis_rateHigh correlation
hiv_diagnoses is highly overall correlated with aids_diagnoses and 4 other fieldsHigh correlation
hiv_diagnosis_rate is highly overall correlated with aids_diagnoses and 3 other fieldsHigh correlation
hiv_related_death_rate is highly overall correlated with death_rate and 2 other fieldsHigh correlation
non_hiv_related_death_rate is highly overall correlated with death_rate and 2 other fieldsHigh correlation
percent_linked_to_care_within_3_months is highly overall correlated with percent_viral_suppression and 1 other fieldsHigh correlation
percent_viral_suppression is highly overall correlated with percent_linked_to_care_within_3_monthsHigh correlation
plwdhi_prevalence is highly overall correlated with aids_diagnosis_rate and 1 other fieldsHigh correlation
uhf is highly overall correlated with boroughHigh correlation
year is highly overall correlated with percent_linked_to_care_within_3_monthsHigh correlation
hiv_diagnoses has 416 (1.3%) missing valuesMissing
hiv_diagnosis_rate has 416 (1.3%) missing valuesMissing
percent_linked_to_care_within_3_months has 13274 (41.6%) missing valuesMissing
aids_diagnoses has 337 (1.1%) missing valuesMissing
aids_diagnosis_rate has 337 (1.1%) missing valuesMissing
plwdhi_prevalence has 3553 (11.1%) missing valuesMissing
percent_viral_suppression has 1913 (6.0%) missing valuesMissing
death_rate has 1913 (6.0%) missing valuesMissing
hiv_related_death_rate has 1913 (6.0%) missing valuesMissing
non_hiv_related_death_rate has 1913 (6.0%) missing valuesMissing
hiv_diagnoses is highly skewed (γ1 = 23.92960598)Skewed
hiv_diagnosis_rate is highly skewed (γ1 = 79.22042465)Skewed
concurrent_diagnoses is highly skewed (γ1 = 23.91290325)Skewed
aids_diagnoses is highly skewed (γ1 = 176.2162638)Skewed
aids_diagnosis_rate is highly skewed (γ1 = 72.48822354)Skewed
plwdhi_prevalence is highly skewed (γ1 = 38.60594909)Skewed
deaths is highly skewed (γ1 = 125.5441877)Skewed
death_rate is highly skewed (γ1 = 20.35510758)Skewed
hiv_diagnoses has 14716 (46.1%) zerosZeros
hiv_diagnosis_rate has 14716 (46.1%) zerosZeros
concurrent_diagnoses has 21904 (68.6%) zerosZeros
percent_linked_to_care_within_3_months has 1114 (3.5%) zerosZeros
aids_diagnoses has 16621 (52.1%) zerosZeros
aids_diagnosis_rate has 16621 (52.1%) zerosZeros
plwdhi_prevalence has 3302 (10.3%) zerosZeros
deaths has 17384 (54.5%) zerosZeros
death_rate has 18051 (56.5%) zerosZeros
hiv_related_death_rate has 22734 (71.2%) zerosZeros
non_hiv_related_death_rate has 20497 (64.2%) zerosZeros

Reproduction

Analysis started2024-05-08 08:42:35.177323
Analysis finished2024-05-08 08:42:49.601427
Duration14.42 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

year
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.8714
Minimum2011
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:49.658408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2012
Q12017
median2018
Q32020
95-th percentile2021
Maximum2021
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7382534
Coefficient of variation (CV)0.0013570009
Kurtosis0.20240171
Mean2017.8714
Median Absolute Deviation (MAD)2
Skewness-1.0023266
Sum64420545
Variance7.4980318
MonotonicityNot monotonic
2024-05-08T04:42:49.726530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2017 5184
16.2%
2018 5184
16.2%
2019 5184
16.2%
2020 5184
16.2%
2021 5184
16.2%
2011 1201
 
3.8%
2012 1201
 
3.8%
2013 1201
 
3.8%
2014 1201
 
3.8%
2015 1201
 
3.8%
ValueCountFrequency (%)
2011 1201
 
3.8%
2012 1201
 
3.8%
2013 1201
 
3.8%
2014 1201
 
3.8%
2015 1201
 
3.8%
2017 5184
16.2%
2018 5184
16.2%
2019 5184
16.2%
2020 5184
16.2%
2021 5184
16.2%
ValueCountFrequency (%)
2021 5184
16.2%
2020 5184
16.2%
2019 5184
16.2%
2018 5184
16.2%
2017 5184
16.2%
2015 1201
 
3.8%
2014 1201
 
3.8%
2013 1201
 
3.8%
2012 1201
 
3.8%
2011 1201
 
3.8%

borough
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size498.8 KiB
Brooklyn
7980 
Manhattan
7315 
Queens
7315 
Bronx
5320 
Staten Island
3325 

Length

Max length13
Median length8
Mean length7.6867659
Min length3

Characters and Unicode

Total characters245400
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowAll
3rd rowAll
4th rowAll
5th rowAll

Common Values

ValueCountFrequency (%)
Brooklyn 7980
25.0%
Manhattan 7315
22.9%
Queens 7315
22.9%
Bronx 5320
16.7%
Staten Island 3325
10.4%
All 670
 
2.1%

Length

2024-05-08T04:42:49.800636image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-08T04:42:49.881665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
brooklyn 7980
22.6%
manhattan 7315
20.8%
queens 7315
20.8%
bronx 5320
15.1%
staten 3325
9.4%
island 3325
9.4%
all 670
 
1.9%

Most occurring characters

ValueCountFrequency (%)
n 41895
17.1%
a 28595
11.7%
t 21280
 
8.7%
o 21280
 
8.7%
e 17955
 
7.3%
r 13300
 
5.4%
B 13300
 
5.4%
l 12645
 
5.2%
s 10640
 
4.3%
y 7980
 
3.3%
Other values (11) 56530
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 206825
84.3%
Uppercase Letter 35250
 
14.4%
Space Separator 3325
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 41895
20.3%
a 28595
13.8%
t 21280
10.3%
o 21280
10.3%
e 17955
8.7%
r 13300
 
6.4%
l 12645
 
6.1%
s 10640
 
5.1%
y 7980
 
3.9%
k 7980
 
3.9%
Other values (4) 23275
11.3%
Uppercase Letter
ValueCountFrequency (%)
B 13300
37.7%
M 7315
20.8%
Q 7315
20.8%
S 3325
 
9.4%
I 3325
 
9.4%
A 670
 
1.9%
Space Separator
ValueCountFrequency (%)
3325
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 242075
98.6%
Common 3325
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 41895
17.3%
a 28595
11.8%
t 21280
 
8.8%
o 21280
 
8.8%
e 17955
 
7.4%
r 13300
 
5.5%
B 13300
 
5.5%
l 12645
 
5.2%
s 10640
 
4.4%
y 7980
 
3.3%
Other values (10) 53205
22.0%
Common
ValueCountFrequency (%)
3325
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 245400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 41895
17.1%
a 28595
11.7%
t 21280
 
8.7%
o 21280
 
8.7%
e 17955
 
7.3%
r 13300
 
5.4%
B 13300
 
5.4%
l 12645
 
5.2%
s 10640
 
4.3%
y 7980
 
3.3%
Other values (11) 56530
23.0%

uhf
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size498.8 KiB
All
3995 
Canarsie - Flatlands
 
665
Fordham - Bronx Park
 
665
High Bridge - Morrisania
 
665
Hunts Point - Mott Haven
 
665
Other values (38)
25270 

Length

Max length36
Median length27
Mean length17.518559
Min length3

Characters and Unicode

Total characters559280
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowAll
3rd rowAll
4th rowAll
5th rowAll

Common Values

ValueCountFrequency (%)
All 3995
 
12.5%
Canarsie - Flatlands 665
 
2.1%
Fordham - Bronx Park 665
 
2.1%
High Bridge - Morrisania 665
 
2.1%
Hunts Point - Mott Haven 665
 
2.1%
Kingsbridge - Riverdale 665
 
2.1%
Northeast Bronx 665
 
2.1%
Pelham - Throgs Neck 665
 
2.1%
Bedford Stuyvesant - Crown Heights 665
 
2.1%
Bensonhurst - Bay Ridge 665
 
2.1%
Other values (33) 21945
68.7%

Length

2024-05-08T04:42:50.094141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17290
 
18.2%
all 3995
 
4.2%
park 3325
 
3.5%
east 3325
 
3.5%
heights 2660
 
2.8%
side 1995
 
2.1%
queens 1995
 
2.1%
harlem 1330
 
1.4%
bronx 1330
 
1.4%
upper 1330
 
1.4%
Other values (79) 56525
59.4%

Most occurring characters

ValueCountFrequency (%)
63175
 
11.3%
e 46550
 
8.3%
a 36575
 
6.5%
o 33250
 
5.9%
t 32585
 
5.8%
r 30590
 
5.5%
n 29260
 
5.2%
s 29260
 
5.2%
l 27940
 
5.0%
i 27265
 
4.9%
Other values (40) 202830
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 400340
71.6%
Uppercase Letter 77810
 
13.9%
Space Separator 63175
 
11.3%
Dash Punctuation 17290
 
3.1%
Other Punctuation 665
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 46550
11.6%
a 36575
9.1%
o 33250
 
8.3%
t 32585
 
8.1%
r 30590
 
7.6%
n 29260
 
7.3%
s 29260
 
7.3%
l 27940
 
7.0%
i 27265
 
6.8%
h 18620
 
4.7%
Other values (14) 88445
22.1%
Uppercase Letter
ValueCountFrequency (%)
S 9310
12.0%
B 7315
 
9.4%
H 7315
 
9.4%
C 5985
 
7.7%
P 5320
 
6.8%
A 4660
 
6.0%
F 4655
 
6.0%
M 3990
 
5.1%
W 3325
 
4.3%
E 3325
 
4.3%
Other values (13) 22610
29.1%
Space Separator
ValueCountFrequency (%)
63175
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17290
100.0%
Other Punctuation
ValueCountFrequency (%)
. 665
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 478150
85.5%
Common 81130
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 46550
 
9.7%
a 36575
 
7.6%
o 33250
 
7.0%
t 32585
 
6.8%
r 30590
 
6.4%
n 29260
 
6.1%
s 29260
 
6.1%
l 27940
 
5.8%
i 27265
 
5.7%
h 18620
 
3.9%
Other values (37) 166255
34.8%
Common
ValueCountFrequency (%)
63175
77.9%
- 17290
 
21.3%
. 665
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 559280
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
63175
 
11.3%
e 46550
 
8.3%
a 36575
 
6.5%
o 33250
 
5.9%
t 32585
 
5.8%
r 30590
 
5.5%
n 29260
 
5.2%
s 29260
 
5.2%
l 27940
 
5.0%
i 27265
 
4.9%
Other values (40) 202830
36.3%

gender
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size498.8 KiB
All
8880 
Men
8640 
Women
8640 
Male
2880 
Female
2880 

Length

Max length11
Median length3
Mean length3.9033673
Min length3

Characters and Unicode

Total characters124615
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowMale
3rd rowFemale
4th rowTransgender
5th rowFemale

Common Values

ValueCountFrequency (%)
All 8880
27.8%
Men 8640
27.1%
Women 8640
27.1%
Male 2880
 
9.0%
Female 2880
 
9.0%
Transgender 5
 
< 0.1%

Length

2024-05-08T04:42:50.178648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-08T04:42:50.252859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
all 8880
27.8%
men 8640
27.1%
women 8640
27.1%
male 2880
 
9.0%
female 2880
 
9.0%
transgender 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 25930
20.8%
l 23520
18.9%
n 17290
13.9%
M 11520
9.2%
m 11520
9.2%
A 8880
 
7.1%
W 8640
 
6.9%
o 8640
 
6.9%
a 5765
 
4.6%
F 2880
 
2.3%
Other values (5) 30
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92690
74.4%
Uppercase Letter 31925
 
25.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 25930
28.0%
l 23520
25.4%
n 17290
18.7%
m 11520
12.4%
o 8640
 
9.3%
a 5765
 
6.2%
r 10
 
< 0.1%
s 5
 
< 0.1%
g 5
 
< 0.1%
d 5
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 11520
36.1%
A 8880
27.8%
W 8640
27.1%
F 2880
 
9.0%
T 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 124615
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 25930
20.8%
l 23520
18.9%
n 17290
13.9%
M 11520
9.2%
m 11520
9.2%
A 8880
 
7.1%
W 8640
 
6.9%
o 8640
 
6.9%
a 5765
 
4.6%
F 2880
 
2.3%
Other values (5) 30
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124615
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 25930
20.8%
l 23520
18.9%
n 17290
13.9%
M 11520
9.2%
m 11520
9.2%
A 8880
 
7.1%
W 8640
 
6.9%
o 8640
 
6.9%
a 5765
 
4.6%
F 2880
 
2.3%
Other values (5) 30
 
< 0.1%

age
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size498.8 KiB
All
7445 
30 - 39
4800 
40 - 49
4800 
50 - 59
4800 
60+
4800 
Other values (3)
5280 

Length

Max length7
Median length7
Mean length5.4657792
Min length3

Characters and Unicode

Total characters174495
Distinct characters14
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowAll
3rd rowAll
4th rowAll
5th row13 - 19

Common Values

ValueCountFrequency (%)
All 7445
23.3%
30 - 39 4800
15.0%
40 - 49 4800
15.0%
50 - 59 4800
15.0%
60+ 4800
15.0%
18 - 29 4320
13.5%
13 - 19 480
 
1.5%
20 - 29 480
 
1.5%

Length

2024-05-08T04:42:50.337834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-08T04:42:50.416250image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
19680
27.6%
all 7445
 
10.4%
30 4800
 
6.7%
39 4800
 
6.7%
40 4800
 
6.7%
49 4800
 
6.7%
50 4800
 
6.7%
59 4800
 
6.7%
60 4800
 
6.7%
29 4800
 
6.7%
Other values (4) 5760
 
8.1%

Most occurring characters

ValueCountFrequency (%)
39360
22.6%
0 19680
11.3%
- 19680
11.3%
9 19680
11.3%
l 14890
 
8.5%
3 10080
 
5.8%
4 9600
 
5.5%
5 9600
 
5.5%
A 7445
 
4.3%
1 5280
 
3.0%
Other values (4) 19200
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88320
50.6%
Space Separator 39360
22.6%
Dash Punctuation 19680
 
11.3%
Lowercase Letter 14890
 
8.5%
Uppercase Letter 7445
 
4.3%
Math Symbol 4800
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19680
22.3%
9 19680
22.3%
3 10080
11.4%
4 9600
10.9%
5 9600
10.9%
1 5280
 
6.0%
2 5280
 
6.0%
6 4800
 
5.4%
8 4320
 
4.9%
Space Separator
ValueCountFrequency (%)
39360
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19680
100.0%
Lowercase Letter
ValueCountFrequency (%)
l 14890
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 7445
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 152160
87.2%
Latin 22335
 
12.8%

Most frequent character per script

Common
ValueCountFrequency (%)
39360
25.9%
0 19680
12.9%
- 19680
12.9%
9 19680
12.9%
3 10080
 
6.6%
4 9600
 
6.3%
5 9600
 
6.3%
1 5280
 
3.5%
2 5280
 
3.5%
6 4800
 
3.2%
Other values (2) 9120
 
6.0%
Latin
ValueCountFrequency (%)
l 14890
66.7%
A 7445
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39360
22.6%
0 19680
11.3%
- 19680
11.3%
9 19680
11.3%
l 14890
 
8.5%
3 10080
 
5.8%
4 9600
 
5.5%
5 9600
 
5.5%
A 7445
 
4.3%
1 5280
 
3.0%
Other values (4) 19200
11.0%

race
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size498.8 KiB
All
7925 
Asian/Pacific Islander
4800 
Black
4800 
Other/Unknown
4800 
White
4800 
Other values (2)
4800 

Length

Max length22
Median length15
Mean length9.7658575
Min length3

Characters and Unicode

Total characters311775
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowAll
3rd rowAll
4th rowAll
5th rowAll

Common Values

ValueCountFrequency (%)
All 7925
24.8%
Asian/Pacific Islander 4800
15.0%
Black 4800
15.0%
Other/Unknown 4800
15.0%
White 4800
15.0%
Latinx/Hispanic 4320
13.5%
Latino/Hispanic 480
 
1.5%

Length

2024-05-08T04:42:50.513700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-08T04:42:50.594417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
all 7925
21.6%
asian/pacific 4800
13.1%
islander 4800
13.1%
black 4800
13.1%
other/unknown 4800
13.1%
white 4800
13.1%
latinx/hispanic 4320
11.8%
latino/hispanic 480
 
1.3%

Most occurring characters

ValueCountFrequency (%)
i 33600
 
10.8%
n 33600
 
10.8%
a 28800
 
9.2%
l 25450
 
8.2%
c 19200
 
6.2%
e 14400
 
4.6%
s 14400
 
4.6%
/ 14400
 
4.6%
t 14400
 
4.6%
A 12725
 
4.1%
Other values (18) 100800
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 241450
77.4%
Uppercase Letter 51125
 
16.4%
Other Punctuation 14400
 
4.6%
Space Separator 4800
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 33600
13.9%
n 33600
13.9%
a 28800
11.9%
l 25450
10.5%
c 19200
8.0%
e 14400
 
6.0%
s 14400
 
6.0%
t 14400
 
6.0%
h 9600
 
4.0%
k 9600
 
4.0%
Other values (7) 38400
15.9%
Uppercase Letter
ValueCountFrequency (%)
A 12725
24.9%
I 4800
 
9.4%
B 4800
 
9.4%
O 4800
 
9.4%
P 4800
 
9.4%
U 4800
 
9.4%
W 4800
 
9.4%
L 4800
 
9.4%
H 4800
 
9.4%
Other Punctuation
ValueCountFrequency (%)
/ 14400
100.0%
Space Separator
ValueCountFrequency (%)
4800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 292575
93.8%
Common 19200
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 33600
 
11.5%
n 33600
 
11.5%
a 28800
 
9.8%
l 25450
 
8.7%
c 19200
 
6.6%
e 14400
 
4.9%
s 14400
 
4.9%
t 14400
 
4.9%
A 12725
 
4.3%
h 9600
 
3.3%
Other values (16) 86400
29.5%
Common
ValueCountFrequency (%)
/ 14400
75.0%
4800
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 311775
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 33600
 
10.8%
n 33600
 
10.8%
a 28800
 
9.2%
l 25450
 
8.2%
c 19200
 
6.2%
e 14400
 
4.6%
s 14400
 
4.6%
/ 14400
 
4.6%
t 14400
 
4.6%
A 12725
 
4.1%
Other values (18) 100800
32.3%

hiv_diagnoses
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct409
Distinct (%)1.3%
Missing416
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean10.936939
Minimum0
Maximum3379
Zeros14716
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:50.684849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile38
Maximum3379
Range3379
Interquartile range (IQR)4

Descriptive statistics

Standard deviation68.89396
Coefficient of variation (CV)6.2991996
Kurtosis822.0801
Mean10.936939
Median Absolute Deviation (MAD)1
Skewness23.929606
Sum344612
Variance4746.3777
MonotonicityNot monotonic
2024-05-08T04:42:50.777625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14716
46.1%
1 4396
 
13.8%
2 2307
 
7.2%
3 1483
 
4.6%
4 1073
 
3.4%
5 823
 
2.6%
6 670
 
2.1%
7 510
 
1.6%
8 443
 
1.4%
9 350
 
1.1%
Other values (399) 4738
 
14.8%
(Missing) 416
 
1.3%
ValueCountFrequency (%)
0 14716
46.1%
1 4396
 
13.8%
2 2307
 
7.2%
3 1483
 
4.6%
4 1073
 
3.4%
5 823
 
2.6%
6 670
 
2.1%
7 510
 
1.6%
8 443
 
1.4%
9 350
 
1.1%
ValueCountFrequency (%)
3379 1
< 0.1%
3106 1
< 0.1%
2856 1
< 0.1%
2749 1
< 0.1%
2595 1
< 0.1%
2490 1
< 0.1%
2436 1
< 0.1%
2265 1
< 0.1%
2177 1
< 0.1%
2007 1
< 0.1%

hiv_diagnosis_rate
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1963
Distinct (%)6.2%
Missing416
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean39.139789
Minimum0
Maximum99999
Zeros14716
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:50.877244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.5
Q329.3
95-th percentile102.16
Maximum99999
Range99999
Interquartile range (IQR)29.3

Descriptive statistics

Standard deviation1260.1023
Coefficient of variation (CV)32.194919
Kurtosis6282.0851
Mean39.139789
Median Absolute Deviation (MAD)4.5
Skewness79.220425
Sum1233255.6
Variance1587857.9
MonotonicityNot monotonic
2024-05-08T04:42:50.972222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14716
46.1%
5.2 61
 
0.2%
11.2 57
 
0.2%
8.7 54
 
0.2%
6 51
 
0.2%
5.5 51
 
0.2%
8.8 51
 
0.2%
4.9 51
 
0.2%
8 50
 
0.2%
8.5 50
 
0.2%
Other values (1953) 16317
51.1%
(Missing) 416
 
1.3%
ValueCountFrequency (%)
0 14716
46.1%
0.2 1
 
< 0.1%
0.3 1
 
< 0.1%
0.4 3
 
< 0.1%
0.5 4
 
< 0.1%
0.6 2
 
< 0.1%
0.7 3
 
< 0.1%
0.8 10
 
< 0.1%
0.9 20
 
0.1%
1 12
 
< 0.1%
ValueCountFrequency (%)
99999 5
< 0.1%
1221.5 1
 
< 0.1%
961.4 1
 
< 0.1%
916.7 1
 
< 0.1%
773.6 1
 
< 0.1%
742.4 1
 
< 0.1%
663.5 1
 
< 0.1%
635.3 1
 
< 0.1%
592.9 1
 
< 0.1%
572 1
 
< 0.1%

concurrent_diagnoses
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct157
Distinct (%)0.5%
Missing116
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean2.066302
Minimum0
Maximum640
Zeros21904
Zeros (%)68.6%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:51.061314image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile8
Maximum640
Range640
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12.799644
Coefficient of variation (CV)6.1944694
Kurtosis832.6
Mean2.066302
Median Absolute Deviation (MAD)0
Skewness23.912903
Sum65727
Variance163.8309
MonotonicityNot monotonic
2024-05-08T04:42:51.169802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21904
68.6%
1 4098
 
12.8%
2 1722
 
5.4%
3 951
 
3.0%
4 592
 
1.9%
5 422
 
1.3%
6 286
 
0.9%
7 219
 
0.7%
8 172
 
0.5%
10 129
 
0.4%
Other values (147) 1314
 
4.1%
(Missing) 116
 
0.4%
ValueCountFrequency (%)
0 21904
68.6%
1 4098
 
12.8%
2 1722
 
5.4%
3 951
 
3.0%
4 592
 
1.9%
5 422
 
1.3%
6 286
 
0.9%
7 219
 
0.7%
8 172
 
0.5%
9 115
 
0.4%
ValueCountFrequency (%)
640 1
< 0.1%
583 1
< 0.1%
564 1
< 0.1%
490 1
< 0.1%
480 1
< 0.1%
452 1
< 0.1%
443 1
< 0.1%
438 1
< 0.1%
369 1
< 0.1%
349 1
< 0.1%

percent_linked_to_care_within_3_months
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct125
Distinct (%)0.7%
Missing13274
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean8178.2828
Minimum0
Maximum99999
Zeros1114
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:51.268894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.81
median1
Q367
95-th percentile99999
Maximum99999
Range99999
Interquartile range (IQR)66.19

Descriptive statistics

Standard deviation27371.207
Coefficient of variation (CV)3.346816
Kurtosis7.345388
Mean8178.2828
Median Absolute Deviation (MAD)0.25
Skewness3.0568908
Sum1.5253315 × 108
Variance7.4918299 × 108
MonotonicityNot monotonic
2024-05-08T04:42:51.366065image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5915
18.5%
99999 1522
 
4.8%
100 1145
 
3.6%
0 1114
 
3.5%
0.5 712
 
2.2%
0.67 600
 
1.9%
0.75 509
 
1.6%
0.8 408
 
1.3%
0.83 384
 
1.2%
67 314
 
1.0%
Other values (115) 6028
18.9%
(Missing) 13274
41.6%
ValueCountFrequency (%)
0 1114
3.5%
0.14 1
 
< 0.1%
0.2 3
 
< 0.1%
0.25 19
 
0.1%
0.29 2
 
< 0.1%
0.33 141
 
0.4%
0.38 2
 
< 0.1%
0.4 19
 
0.1%
0.41 1
 
< 0.1%
0.42 2
 
< 0.1%
ValueCountFrequency (%)
99999 1522
4.8%
100 1145
3.6%
95 5
 
< 0.1%
94 9
 
< 0.1%
93 7
 
< 0.1%
92 17
 
0.1%
91 13
 
< 0.1%
90 19
 
0.1%
89 27
 
0.1%
88 46
 
0.1%

aids_diagnoses
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct313
Distinct (%)1.0%
Missing337
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean9.9899329
Minimum0
Maximum99999
Zeros16621
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:51.459281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile25
Maximum99999
Range99999
Interquartile range (IQR)3

Descriptive statistics

Standard deviation564.22499
Coefficient of variation (CV)56.479358
Kurtosis31227.17
Mean9.9899329
Median Absolute Deviation (MAD)0
Skewness176.21626
Sum315562
Variance318349.84
MonotonicityNot monotonic
2024-05-08T04:42:51.555825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16621
52.1%
1 4397
 
13.8%
2 2213
 
6.9%
3 1437
 
4.5%
4 959
 
3.0%
5 770
 
2.4%
6 592
 
1.9%
7 450
 
1.4%
8 351
 
1.1%
9 333
 
1.0%
Other values (303) 3465
 
10.9%
(Missing) 337
 
1.1%
ValueCountFrequency (%)
0 16621
52.1%
1 4397
 
13.8%
2 2213
 
6.9%
3 1437
 
4.5%
4 959
 
3.0%
5 770
 
2.4%
6 592
 
1.9%
7 450
 
1.4%
8 351
 
1.1%
9 333
 
1.0%
ValueCountFrequency (%)
99999 1
< 0.1%
2366 1
< 0.1%
2106 1
< 0.1%
1949 1
< 0.1%
1712 1
< 0.1%
1529 1
< 0.1%
1518 1
< 0.1%
1440 1
< 0.1%
1307 1
< 0.1%
1087 1
< 0.1%

aids_diagnosis_rate
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1525
Distinct (%)4.8%
Missing337
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean33.94949
Minimum0
Maximum99999
Zeros16621
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:51.652670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q318.2
95-th percentile69.365
Maximum99999
Range99999
Interquartile range (IQR)18.2

Descriptive statistics

Standard deviation1378.2115
Coefficient of variation (CV)40.595942
Kurtosis5255.4117
Mean33.94949
Median Absolute Deviation (MAD)0
Skewness72.488224
Sum1072396.5
Variance1899467
MonotonicityNot monotonic
2024-05-08T04:42:51.760159image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16621
52.1%
5.6 81
 
0.3%
5.5 73
 
0.2%
5.1 72
 
0.2%
10.6 67
 
0.2%
5.3 65
 
0.2%
5.4 62
 
0.2%
5.7 61
 
0.2%
5.8 61
 
0.2%
5.2 60
 
0.2%
Other values (1515) 14365
45.0%
(Missing) 337
 
1.1%
ValueCountFrequency (%)
0 16621
52.1%
0.4 5
 
< 0.1%
0.5 9
 
< 0.1%
0.7 14
 
< 0.1%
0.8 24
 
0.1%
0.9 17
 
0.1%
1 13
 
< 0.1%
1.1 25
 
0.1%
1.2 32
 
0.1%
1.3 17
 
0.1%
ValueCountFrequency (%)
99999 6
< 0.1%
588 1
 
< 0.1%
581.7 1
 
< 0.1%
539.8 1
 
< 0.1%
494.5 1
 
< 0.1%
418.5 1
 
< 0.1%
414.3 1
 
< 0.1%
403.5 1
 
< 0.1%
392.9 1
 
< 0.1%
386.8 1
 
< 0.1%

plwdhi_prevalence
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct169
Distinct (%)0.6%
Missing3553
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean68.20455
Minimum0
Maximum99999
Zeros3302
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:51.855463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median0.6
Q31.6
95-th percentile4.6
Maximum99999
Range99999
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation2586.9283
Coefficient of variation (CV)37.928969
Kurtosis1488.5249
Mean68.20455
Median Absolute Deviation (MAD)0.5
Skewness38.605949
Sum1935099.5
Variance6692198
MonotonicityNot monotonic
2024-05-08T04:42:51.949429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 3336
 
10.4%
0 3302
 
10.3%
0.2 2480
 
7.8%
0.3 1852
 
5.8%
0.4 1585
 
5.0%
0.5 1310
 
4.1%
0.6 1113
 
3.5%
0.7 945
 
3.0%
0.8 821
 
2.6%
0.9 709
 
2.2%
Other values (159) 10919
34.2%
(Missing) 3553
 
11.1%
ValueCountFrequency (%)
0 3302
10.3%
0.1 3336
10.4%
0.2 2480
7.8%
0.3 1852
5.8%
0.4 1585
5.0%
0.5 1310
 
4.1%
0.6 1113
 
3.5%
0.7 945
 
3.0%
0.8 821
 
2.6%
0.9 709
 
2.2%
ValueCountFrequency (%)
99999 19
0.1%
27.9 1
 
< 0.1%
26.6 1
 
< 0.1%
26.1 1
 
< 0.1%
23.4 1
 
< 0.1%
23.3 1
 
< 0.1%
22.6 1
 
< 0.1%
22.4 1
 
< 0.1%
20.8 1
 
< 0.1%
20.3 1
 
< 0.1%

percent_viral_suppression
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct170
Distinct (%)0.6%
Missing1913
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean532.08241
Minimum0
Maximum99999
Zeros292
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:52.044625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q10.8
median0.9
Q31
95-th percentile87
Maximum99999
Range99999
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation7166.9219
Coefficient of variation (CV)13.469571
Kurtosis188.65555
Mean532.08241
Median Absolute Deviation (MAD)0.1
Skewness13.807222
Sum15968857
Variance51364769
MonotonicityNot monotonic
2024-05-08T04:42:52.157937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5193
 
16.3%
0.8 831
 
2.6%
0.86 819
 
2.6%
0.88 796
 
2.5%
0.89 781
 
2.4%
0.83 758
 
2.4%
0.9 703
 
2.2%
0.87 702
 
2.2%
0.81 677
 
2.1%
0.85 667
 
2.1%
Other values (160) 18085
56.6%
(Missing) 1913
 
6.0%
ValueCountFrequency (%)
0 292
0.9%
0.06 3
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 2
 
< 0.1%
0.12 5
 
< 0.1%
0.13 3
 
< 0.1%
0.14 1
 
< 0.1%
0.16 2
 
< 0.1%
0.17 3
 
< 0.1%
ValueCountFrequency (%)
99999 155
 
0.5%
100 418
1.3%
99 1
 
< 0.1%
98 3
 
< 0.1%
97 15
 
< 0.1%
96 20
 
0.1%
95 35
 
0.1%
94 38
 
0.1%
93 59
 
0.2%
92 95
 
0.3%

deaths
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct374
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.97397
Minimum0
Maximum99999
Zeros17384
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:52.249575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile31
Maximum99999
Range99999
Interquartile range (IQR)3

Descriptive statistics

Standard deviation793.08115
Coefficient of variation (CV)52.963986
Kurtosis15825.747
Mean14.97397
Median Absolute Deviation (MAD)0
Skewness125.54419
Sum478044
Variance628977.71
MonotonicityNot monotonic
2024-05-08T04:42:52.449909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17384
54.5%
1 3937
 
12.3%
2 1994
 
6.2%
3 1247
 
3.9%
4 873
 
2.7%
5 702
 
2.2%
6 520
 
1.6%
7 461
 
1.4%
8 362
 
1.1%
9 335
 
1.0%
Other values (364) 4110
 
12.9%
ValueCountFrequency (%)
0 17384
54.5%
1 3937
 
12.3%
2 1994
 
6.2%
3 1247
 
3.9%
4 873
 
2.7%
5 702
 
2.2%
6 520
 
1.6%
7 461
 
1.4%
8 362
 
1.1%
9 335
 
1.0%
ValueCountFrequency (%)
99999 2
< 0.1%
2040 1
< 0.1%
1906 1
< 0.1%
1898 1
< 0.1%
1824 1
< 0.1%
1751 1
< 0.1%
1678 1
< 0.1%
1645 1
< 0.1%
1423 1
< 0.1%
1375 1
< 0.1%

death_rate
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct757
Distinct (%)2.5%
Missing1913
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean7.3827736
Minimum0
Maximum1000
Zeros18051
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:52.547649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.9
95-th percentile27.3
Maximum1000
Range1000
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation32.033529
Coefficient of variation (CV)4.3389559
Kurtosis551.30993
Mean7.3827736
Median Absolute Deviation (MAD)0
Skewness20.355108
Sum221571.8
Variance1026.147
MonotonicityNot monotonic
2024-05-08T04:42:52.646092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18051
56.5%
6.4 95
 
0.3%
5.7 94
 
0.3%
7.8 90
 
0.3%
7.3 88
 
0.3%
7.1 88
 
0.3%
8.3 88
 
0.3%
6.8 87
 
0.3%
4.6 86
 
0.3%
5.9 84
 
0.3%
Other values (747) 11161
35.0%
(Missing) 1913
 
6.0%
ValueCountFrequency (%)
0 18051
56.5%
0.3 1
 
< 0.1%
0.6 3
 
< 0.1%
0.7 3
 
< 0.1%
0.8 8
 
< 0.1%
0.9 7
 
< 0.1%
1 11
 
< 0.1%
1.1 8
 
< 0.1%
1.2 14
 
< 0.1%
1.3 11
 
< 0.1%
ValueCountFrequency (%)
1000 16
0.1%
817.1 1
 
< 0.1%
500 19
0.1%
478.5 1
 
< 0.1%
394.8 1
 
< 0.1%
348.4 1
 
< 0.1%
333.3 17
0.1%
314.5 1
 
< 0.1%
296.9 1
 
< 0.1%
284.6 1
 
< 0.1%

hiv_related_death_rate
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct422
Distinct (%)1.4%
Missing1913
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean4003.3887
Minimum0
Maximum99999
Zeros22734
Zeros (%)71.2%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:52.810177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile24.6
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19599.773
Coefficient of variation (CV)4.8957955
Kurtosis20.034381
Mean4003.3887
Median Absolute Deviation (MAD)0
Skewness4.6939361
Sum1.201497 × 108
Variance3.8415109 × 108
MonotonicityNot monotonic
2024-05-08T04:42:52.909829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22734
71.2%
99999 1201
 
3.8%
2.6 123
 
0.4%
1.9 121
 
0.4%
1.8 117
 
0.4%
1.7 114
 
0.4%
2.4 106
 
0.3%
2.1 105
 
0.3%
1.5 105
 
0.3%
1.4 103
 
0.3%
Other values (412) 5183
 
16.2%
(Missing) 1913
 
6.0%
ValueCountFrequency (%)
0 22734
71.2%
0.2 4
 
< 0.1%
0.3 8
 
< 0.1%
0.4 27
 
0.1%
0.5 30
 
0.1%
0.6 40
 
0.1%
0.7 70
 
0.2%
0.8 53
 
0.2%
0.9 72
 
0.2%
1 72
 
0.2%
ValueCountFrequency (%)
99999 1201
3.8%
1000 1
 
< 0.1%
500 6
 
< 0.1%
394.8 1
 
< 0.1%
333.3 2
 
< 0.1%
264 1
 
< 0.1%
250 2
 
< 0.1%
200 3
 
< 0.1%
172.2 5
 
< 0.1%
163 1
 
< 0.1%

non_hiv_related_death_rate
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct589
Distinct (%)2.0%
Missing1913
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean4005.7664
Minimum0
Maximum99999
Zeros20497
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size498.8 KiB
2024-05-08T04:42:53.001351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.2
95-th percentile48.145
Maximum99999
Range99999
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation19599.299
Coefficient of variation (CV)4.8927714
Kurtosis20.034325
Mean4005.7664
Median Absolute Deviation (MAD)0
Skewness4.6939268
Sum1.2022106 × 108
Variance3.8413253 × 108
MonotonicityNot monotonic
2024-05-08T04:42:53.095953image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20497
64.2%
99999 1201
 
3.8%
4.9 96
 
0.3%
3.8 93
 
0.3%
3.9 92
 
0.3%
3.4 88
 
0.3%
2.9 86
 
0.3%
5.3 85
 
0.3%
4.4 83
 
0.3%
5.5 83
 
0.3%
Other values (579) 7608
 
23.8%
(Missing) 1913
 
6.0%
ValueCountFrequency (%)
0 20497
64.2%
0.3 2
 
< 0.1%
0.4 2
 
< 0.1%
0.5 6
 
< 0.1%
0.6 10
 
< 0.1%
0.7 12
 
< 0.1%
0.8 20
 
0.1%
0.9 19
 
0.1%
1 23
 
0.1%
1.1 23
 
0.1%
ValueCountFrequency (%)
99999 1201
3.8%
1000 11
 
< 0.1%
500 9
 
< 0.1%
348.4 1
 
< 0.1%
333.3 11
 
< 0.1%
314.5 1
 
< 0.1%
250 6
 
< 0.1%
246.3 1
 
< 0.1%
238 1
 
< 0.1%
222.2 1
 
< 0.1%

Interactions

2024-05-08T04:42:37.247899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:38.210949image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:39.369248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.291299image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:41.156295image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:42.007635image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.014799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.875316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.714660image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.573684image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:46.565866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:37.323416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:38.277437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:39.431411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.356288image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:41.220429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:42.070972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.074956image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.936853image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.779321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.638693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:46.625936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:37.395393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:38.345875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:39.502842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.425613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:41.294601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:42.138307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.144062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.003719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.849579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.704913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:46.692985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:37.457580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:38.411470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:39.573995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.492259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:41.358851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:42.373570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.230888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.065469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.915157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.768243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:46.755632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:37.528550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:38.477515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:39.644002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.557758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:41.424441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:42.437573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.295513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.135471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.985544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.831723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:46.820802image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:37.592512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:38.547962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:39.713361image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.625348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:41.493357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:42.506725image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.360476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.200030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.048548image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.897344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:46.913776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:37.655503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:38.614586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:39.779086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.690896image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:41.561041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:42.572489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.423149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.263681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.118130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.959805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:46.977458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:37.717627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:38.680316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:39.901195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.754881image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:41.624585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:42.635735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.487125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.328879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.181891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:46.024625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:47.043132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:37.778717image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:38.745154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:39.972056image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.818859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:41.688906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:42.699102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.552307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.392557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.246183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:46.086909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:47.105837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:37.844339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:39.081948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.038017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.884467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:41.752306image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:42.765213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.617347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.455188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.313835image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:46.313484image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:47.169262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:37.907815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:39.166158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.102226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:40.952532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:41.816920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:42.825798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:43.678562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:44.521085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:45.379971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:46.376627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-08T04:42:47.230741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-05-08T04:42:53.173089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ageaids_diagnosesaids_diagnosis_rateboroughconcurrent_diagnosesdeath_ratedeathsgenderhiv_diagnoseshiv_diagnosis_ratehiv_related_death_ratenon_hiv_related_death_ratepercent_linked_to_care_within_3_monthspercent_viral_suppressionplwdhi_prevalenceraceuhfyear
age1.0000.1830.1190.0000.1610.2890.4010.2280.092-0.0090.2240.2860.1540.2600.2080.1590.000-0.141
aids_diagnoses0.1831.0000.9090.0010.8220.5460.7160.0130.8240.6570.4940.470-0.138-0.0490.4810.0000.015-0.170
aids_diagnosis_rate0.1190.9091.0000.0770.6830.4620.5830.9130.6850.6660.3850.377-0.095-0.0990.5620.0140.000-0.146
borough0.0000.0010.0771.000-0.173-0.141-0.1960.036-0.178-0.149-0.108-0.1140.0600.097-0.2430.0000.8570.000
concurrent_diagnoses0.1610.8220.683-0.1731.0000.4480.6130.0340.7900.6260.4460.405-0.0690.0040.3700.0340.049-0.161
death_rate0.2890.5460.462-0.1410.4481.0000.8170.0100.4680.3430.5870.7370.0400.0680.3730.0240.027-0.126
deaths0.4010.7160.583-0.1960.6130.8171.0000.0110.6280.4530.5720.651-0.1130.0090.5350.0130.040-0.111
gender0.2280.0130.9130.0360.0340.0100.0111.000-0.235-0.203-0.149-0.150-0.131-0.142-0.1630.2120.0000.166
hiv_diagnoses0.0920.8240.685-0.1780.7900.4680.628-0.2351.0000.8880.4550.422-0.230-0.0610.4110.0360.050-0.200
hiv_diagnosis_rate-0.0090.6570.666-0.1490.6260.3430.453-0.2030.8881.0000.3240.298-0.198-0.1190.4830.0170.000-0.180
hiv_related_death_rate0.2240.4940.385-0.1080.4460.5870.572-0.1490.4550.3241.0000.6190.2320.2370.2410.1980.000-0.375
non_hiv_related_death_rate0.2860.4700.377-0.1140.4050.7370.651-0.1500.4220.2980.6191.0000.1960.2070.2780.1980.000-0.318
percent_linked_to_care_within_3_months0.154-0.138-0.0950.060-0.0690.040-0.113-0.131-0.230-0.1980.2320.1961.0000.643-0.1910.2910.301-0.615
percent_viral_suppression0.260-0.049-0.0990.0970.0040.0680.009-0.142-0.061-0.1190.2370.2070.6431.000-0.2320.0830.114-0.445
plwdhi_prevalence0.2080.4810.562-0.2430.3700.3730.535-0.1630.4110.4830.2410.278-0.191-0.2321.0000.0290.070-0.006
race0.1590.0000.0140.0000.0340.0240.0130.2120.0360.0170.1980.1980.2910.0830.0291.0000.0000.210
uhf0.0000.0150.0000.8570.0490.0270.0400.0000.0500.0000.0000.0000.3010.1140.0700.0001.0000.000
year-0.141-0.170-0.1460.000-0.161-0.126-0.1110.166-0.200-0.180-0.375-0.318-0.615-0.445-0.0060.2100.0001.000

Missing values

2024-05-08T04:42:49.172626image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-08T04:42:49.372797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearboroughuhfgenderageracehiv_diagnoseshiv_diagnosis_rateconcurrent_diagnosespercent_linked_to_care_within_3_monthsaids_diagnosesaids_diagnosis_rateplwdhi_prevalencepercent_viral_suppressiondeathsdeath_ratehiv_related_death_ratenon_hiv_related_death_rate
02011AllAllAllAllAll3379.048.3640.066.02366.033.81.171.0204013.65.87.8
12011AllAllMaleAllAll2595.079.1480.066.01712.052.21.772.0142313.45.77.7
22011AllAllFemaleAllAll733.021.1153.066.0622.017.60.668.060514.06.08.0
32011AllAllTransgenderAllAll51.099999.07.063.032.099999.099999.055.01211.15.75.4
42011AllAllFemale13 - 19All47.013.64.064.022.06.40.157.011.41.40.0
52011AllAllFemale20 - 29All178.024.720.067.096.013.30.348.0197.23.24.0
62011AllAllFemale30 - 39All176.026.931.066.0133.020.30.661.0539.45.73.7
72011AllAllFemale40 - 49All195.033.050.062.0210.035.51.466.018415.97.88.1
82011AllAllFemale50 - 59All130.023.532.072.0133.024.01.373.023124.111.512.6
92011AllAllFemale60+All57.06.723.068.060.07.10.381.012933.510.622.9
yearboroughuhfgenderageracehiv_diagnoseshiv_diagnosis_rateconcurrent_diagnosespercent_linked_to_care_within_3_monthsaids_diagnosesaids_diagnosis_rateplwdhi_prevalencepercent_viral_suppressiondeathsdeath_ratehiv_related_death_ratenon_hiv_related_death_rate
319152021Staten IslandWillowbrookWomen50 - 59Black0.00.00.0NaN0.00.02.51.000.00.00.0
319162021Staten IslandWillowbrookWomen50 - 59Latinx/Hispanic0.00.00.0NaN0.00.00.81.010.00.00.0
319172021Staten IslandWillowbrookWomen50 - 59Other/Unknown0.00.00.0NaN0.00.00.0NaN0NaNNaNNaN
319182021Staten IslandWillowbrookWomen50 - 59White0.00.00.0NaN0.00.00.21.000.00.00.0
319192021Staten IslandWillowbrookWomen60+All0.00.00.0NaN0.00.0NaN0.800.00.00.0
319202021Staten IslandWillowbrookWomen60+Asian/Pacific Islander0.00.00.0NaN0.00.00.0NaN0NaNNaNNaN
319212021Staten IslandWillowbrookWomen60+Black0.00.00.0NaN0.00.0NaN1.000.00.00.0
319222021Staten IslandWillowbrookWomen60+Latinx/Hispanic0.00.00.0NaN0.00.00.70.500.00.00.0
319232021Staten IslandWillowbrookWomen60+Other/Unknown0.00.00.0NaN0.00.00.0NaN0NaNNaNNaN
319242021Staten IslandWillowbrookWomen60+White0.00.00.0NaN0.00.00.11.000.00.00.0